102 lines
2.1 KiB
PHP
102 lines
2.1 KiB
PHP
<?php
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declare(strict_types=1);
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namespace Phpml\Regression;
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use Phpml\Helper\Predictable;
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use Phpml\Math\Matrix;
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class LeastSquares implements Regression
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{
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use Predictable;
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/**
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* @var array
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*/
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private $samples = [];
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/**
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* @var array
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*/
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private $targets = [];
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/**
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* @var float
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*/
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private $intercept;
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/**
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* @var array
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*/
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private $coefficients = [];
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public function train(array $samples, array $targets): void
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{
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$this->samples = array_merge($this->samples, $samples);
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$this->targets = array_merge($this->targets, $targets);
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$this->computeCoefficients();
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}
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/**
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* @return mixed
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*/
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public function predictSample(array $sample)
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{
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$result = $this->intercept;
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foreach ($this->coefficients as $index => $coefficient) {
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$result += $coefficient * $sample[$index];
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}
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return $result;
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}
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public function getCoefficients(): array
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{
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return $this->coefficients;
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}
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public function getIntercept(): float
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{
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return $this->intercept;
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}
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/**
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* coefficient(b) = (X'X)-1X'Y.
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*/
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private function computeCoefficients(): void
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{
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$samplesMatrix = $this->getSamplesMatrix();
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$targetsMatrix = $this->getTargetsMatrix();
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$ts = $samplesMatrix->transpose()->multiply($samplesMatrix)->inverse();
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$tf = $samplesMatrix->transpose()->multiply($targetsMatrix);
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$this->coefficients = $ts->multiply($tf)->getColumnValues(0);
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$this->intercept = array_shift($this->coefficients);
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}
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/**
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* Add one dimension for intercept calculation.
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*/
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private function getSamplesMatrix(): Matrix
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{
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$samples = [];
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foreach ($this->samples as $sample) {
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array_unshift($sample, 1);
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$samples[] = $sample;
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}
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return new Matrix($samples);
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}
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private function getTargetsMatrix(): Matrix
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{
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if (is_array($this->targets[0])) {
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return new Matrix($this->targets);
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}
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return Matrix::fromFlatArray($this->targets);
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}
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}
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